Reasoning with uncertainties and/or incompleteness refers to the various processes leading from evidences or clues to conclusions or guesses using uncertain, vague, partial, incomplete and/or limited information. Reasoning with uncertainties mostly refers to information which are uncertain, vague and/or inexact; while reasoning with incompleteness refers to information which are incomplete, partial and/or limited. A knowledge-based system under uncertainties and/or incompleteness is a knowledge base where reasoning with uncertainties and/or incompleteness are involved.
Knowledge-based systems involving uncertainties and/or incompleteness have been studied widely in the literature. Many approaches have been introduced to model such knowledge bases, but none are satisfactory in general.
The basic building blocks of knowledge bases are knowledge, which are usually represented as rules, propositions, or other equivalent means.
Moreover, in most traditional knowledge bases with uncertainties and/or incompleteness, each piece of knowledge in the knowledge base is associated with or mapped to a number, variably referred to as belief, certainty factor, likelihood, weight, etc.
To perform reasoning/inferences, either an extension scheme for the mapping mentioned above, or a conditioning/composition rule must be specified.